Sample-level matching is the other way to match distribution between domains and the representative work is [1]}. Nicolas Courty et al. utilized the theory of optimal transport to learn the coupling between two probability density functions. Through the barycentric mapping [2]}, the images of the source samples in the target domain are obtained, and then a simple classification model can be used to classify the target samples. [3]} extended it which designs a metric learning optimal transport (MLOT) algorithm to optimizes a mahalanobis distance. Besides optimal transport based methods, [4]}, [5]} used hypergraph matching to match the samples between two domains.
